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Grouping Like-Minded Users Based on Text and Sentiment Analysis

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 8733)

Abstract

With the growth of social media usage, the study of online communities and groups has become an appealing research domain. In this context, grouping like-minded users is one of the emerging problems. Indeed, it gives a good idea about group formation and evolution, explains various social phenomena and leads to many applications, such as link prediction and product suggestion. In this dissertation, we propose a novel unsupervised method for grouping like-minded users within social networks. Such a method detects groups of users sharing the same interest centers and having similar opinions. In fact, the proposed method is based on extracting the interest centers and retrieving the polarities from the user’s textual posts.

Keywords

  • Social network
  • like-minded users
  • interest center
  • sentiment analysis

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  • DOI: 10.1007/978-3-319-11289-3_9
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Jaffali, S., Jamoussi, S., Hamadou, A.B. (2014). Grouping Like-Minded Users Based on Text and Sentiment Analysis. In: Hwang, D., Jung, J.J., Nguyen, NT. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2014. Lecture Notes in Computer Science(), vol 8733. Springer, Cham. https://doi.org/10.1007/978-3-319-11289-3_9

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  • DOI: https://doi.org/10.1007/978-3-319-11289-3_9

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11288-6

  • Online ISBN: 978-3-319-11289-3

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